Which safety commitments did they back away from? My understanding is that they believe safety can only be researched from the frontier, and so they're trying to be pragmatic to stay near the frontier (and viable) in their choices.

From what I know, the "books3" dataset was normalised in the LLM and research ecosystem, where collected datasets were seen as valid to train on and/or fair use. I'm not sure any of the major frontier companies are free from that, if we don't believe it was fair use.

I do think most of their choices are explainable by "they just believe in agi risk". You truly wouldn't want non-agi-pilled companies to train on your data and approach the frontier if you were worried. They are less worried about other "moral" decisions like "sharing" if they conflict with AGI. That definitely doesn't make them "good", but they do seem fairly "consistent".

> Anthropic leadership

Which one? The main bit that reports to Daniela Amodei, or the little comfort blanket cabinet around Dario and his "chief of staff"?

There is a leadership branch that can pretend to be morally qualified and aware and to think about the big picture and ethics.

It is at least somewhat remote from the bit that is doing the actual business things.

It makes me sad that people don’t see right through Anthropic’s gambit.

They want to position AI as an insurmountable threat in order to regulate away any future competitors. They’re trying to speedrun regulatory capture.

It's almost like running a trillion-dollar business with neck-to-neck competition with other frontier labs and even state-sponsored efforts requires some ethical trade-off.

Pirating books is just straight up morally correct. I don't like Anthropic's bullshit "safety" filters, but training on shadow library data? Yeah no, it makes sense.

It makes a lot more sense than having to work around copyright by scanning out physical books. Unfortunately, one was ruled legal and the other was not.

Yeah this would be a great point if it were true and they didn’t give Mythos access to companies to fix bugs, which they did and have.

It’s genuinely a difficult question. Not black and white. The models are really good at finding bugs, as demonstrated by people using Fable to reverse engineer. People make it sound like he’s just making it up.

Dario's "ethical" look is also kinda sus. I hate to use ad hominem, but the dude's wife literally pitched a porn film to Epstein even after he was a convicted registered sex offender [1]. Dario is also really sinophobic (it is commonly claimed in Chinese AI circles that his former employment at Baidu triggered him so much that he harbors a personal grudge against the entire race).

[1] https://www.forbes.com/sites/alisondurkee/2026/08/14/who-is-...

Pretty much. That's even worse imho.

I just don’t see how people have looked at what has happened with Mythos and the deluge of fixes from companies, then come to this conclusion.

He has a really hard job. He errs on the side of conservatism in releasing and then people get Really Mad.

Safeguards on cybersecurity are not great for Anthropic revenue! As evidenced by people getting pissed, moving to Sol, and them having a smaller market for what Fable can do.

It’s clearly bad for revenue and not great advertising to say, “you can’t use this but here is a nerfed version that will annoy you and not solve important problems.”

Well what do you want? Presenting clear, desirable, and achievable visions and trying to build consensus for how AI should develop is crucial at this point in time.

Technology doesn't just spring into being, there will always be comments on the organizations that developed it. If you don't like them or find them repetitive, it is far easier to collapse them and move on then bend a stranger to your will

I get it, but the underlying problem is: we don't have a society-wide, effective solution to counterbalancing extractive systems. Lacking a reliable label, we have to constantly signal what's on the ingredients list.

What I'm tired of is the top story (or five) on HN every day announcing Spark Opus Fable Grok Gemini v4.1i3-F. Like, who actually cares? Are people excited for the new benchmarks? Is it interesting to read the model cards? And look, part of my job is to use these things and part of my job is to pick EC2 servers, too. The front page of HN is increasingly resembling one of those endless AWS pricing lists.

And yeah, I don't like any of the people or companies building LLMs either. At least the griping is somewhat interesting by comparison. The model isn't news. The news on Hacker News is that other professionals feel the same way.

> If you don’t like it […], then maybe just […] stay silent.

You might consider following your own advice.

That's not how any of this works my man. Must be nice to think you live a life where neither has had a profound negative impact on your day to day

yeah, we should all just stfu because one internet dude is tired of hearing it

Staying silent is unfortunately how fascism festers.

Zuck's bad PR is to blame here. Not the commenters. He should fix that.

Anduril makes this same complaint whenever their job posts get dumped on. Same idea. Fix your bad PR, buddies :)

Not liking something because the embodiment of corporate malfeasance is a rational way to decide what products to support.

> But the post is about Muse Spark 1.3. What do you think about that?

That:

- like all models it was trained on stolen data

- additionally it was trained on Facebook users who were all opted in to AI training with a convoluted 10+ step process to opt-out of

> If you don’t like it because Meta made it, then maybe just don’t use it and stay silent.

Why should anyone stay silent?

Totally fine with open weight, since other people can provide it and Meta isn’t making money. I’d use an AWS-hosted version.

I am finding Poolside's a decent model.-

He didn't say 90% of the coding jobs. He said LLMs would write 90% of the code. As in be LLM generated.

They don't all suck equally.

Here's the order, from best to worst.

Amodei

Google

SamA

Zuck

Elon

How is this a tangential annoyance or a generic tangent?

> Meta announces they have a new model, demonstrating its capabilities.

> Parent comment states „regardless of this model‘s specific capabilities, if I can avoid it I will.“

"Avoid generic tangents" / "Please don't complain about tangential annoyances."

That's pretty much 90% of HN these days.

Apple releases a new iPhone? Here comes the flood of decade-old complaints about long-discontinued Mac butterfly keyboards and walled gardens.

Microsoft releases a new version of Windows? Here come the gripes about Azure.

Google changes something in GMail? Play Store!

It's like there's an army of bots out there determined to reduce the productivity of the Western tech bubble by diverting everyone into endless circular arguments about absolutely nothing of relevance to the topic at hand.

Enabled genocide in Myanmar

The canonical view of a bicycle is facing right. Usually, people want to draw/photograph/depict the side of the bicycle with the running gear, which is on the right side of the frame for historical reasons.

The more generic your prompt, the more generic the response. It's a regression to the "mean" of the training data aka GIGO for AI.

It's like when you ask your average person off the street to draw a house - it'll almost always be square with a triangle roof, one door, and two windows.

In the pelican/bike example, it's probably a bit of a self-perpetuating snowball too. If the earliest examples were bike left-to-right, flat ground, etc. then they are also being scraped up in future LLMs.

Not when rendered via POV-Ray:

https://blog.nawaz.org/posts/2025/Oct/pelican-on-a-bike-rayt...

I plan to update it with more pelicans from all the models released since.

(Spoiler alert: They haven't improved much since then).

It's really interesting, isn't it? They almost always cycle from left to right - but I have had a few which cycle in the other direction.

The 2D / flat ground feels reasonable for a SVG, which implies a vector illustration.

Search Google Images for "bicycle". Almost all bicycle product shots are staged the same way: side view, going left-to-right. It makes sense to me that given that skew in the training data, the model grounds itself in the bicycle.

Sun is missing a few rays and not wearing sunglasses.

Yes, I do a thing where I ask the machine to generate responses in the form of a lizard talking to a cat. The lizard is always a green gecko and the cat is always orange, which I never specify.

Is there a reason these pelicans always have roughly the same composition

Because they're computers. They don't have an imagination and the ability to create things from whole cloth the way humans do.

Much like a mother pelican, they regurgitate what they've been fed.

It would be very embarrassing for any lab to benchmaxx the pelican on bicycle svg prompt, since it would be very easy to detect it by varying the prompt.

You win this thread's prize:

https://news.ycombinator.com/item?id=49538333

https://en.wikipedia.org/wiki/Bird_feet_and_legs#/media/File...

Bird knees bend same way human ones do

I think it's hilarious that this detail is enough for me to dismiss looking into the model, but here we are, and it is.

Red eyes and a tattoo?

Every lab trains their models with AI generated code at this point.

I would imagine your interactions with it are more important than the output.

Training on ai generated content is how the models got a big jump in capability

You gotta keep up. Fable 5.1 came out yesterday and is better so anything else is to be treated as garbage now.

Muse 1.2 wrote a terrible "smart summaries" extension for my pi setup. It was sending every single steamed chunk for summarization instead of waiting for the full CMD.

This is an error I would expect from sonnet 4, not a model that was supposedly just a few points behind sol.

There's a lot of value in agentic loop tool failure + recovery training data

It's the perfect model for open-source work because it's gonna end up in the training data anyway

Web Search doesn't have a discount on contributor pricing